IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/975953.html
   My bibliography  Save this article

A Bayesian Classifier Learning Algorithm Based on Optimization Model

Author

Listed:
  • Sanyang Liu
  • Mingmin Zhu
  • Youlong Yang

Abstract

Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes and affects its classification performance. In this paper, we summarize the existing improved algorithms and propose a Bayesian classifier learning algorithm based on optimization model (BC-OM). BC-OM uses the chi-squared statistic to estimate the dependence coefficients among attributes, with which it constructs the objective function as an overall measure of the dependence for a classifier structure. Therefore, a problem of searching for an optimal classifier can be turned into finding the maximum value of the objective function in feasible fields. In addition, we have proved the existence and uniqueness of the numerical solution. BC-OM offers a new opinion for the research of extended Bayesian classifier. Theoretical and experimental results show that the new algorithm is correct and effective.

Suggested Citation

  • Sanyang Liu & Mingmin Zhu & Youlong Yang, 2013. "A Bayesian Classifier Learning Algorithm Based on Optimization Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, February.
    • Handle: RePEc:hin:jnlmpe:975953
    DOI: 10.1155/2013/975953
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2013/975953.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2013/975953.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2013/975953?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grzegorz Ostasz & Dominika Siwiec & Andrzej Pacana, 2022. "Universal Model to Predict Expected Direction of Products Quality Improvement," Energies, MDPI, vol. 15(5), pages 1-18, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:975953. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.